MiniMax has two frontier models: M2.5 (February 2026) and M2.7 (March 2026). The newer one isn’t always better — and depending on your workload, M2.5 might actually save you money while delivering equivalent results.

This guide breaks down benchmarks, pricing, API usage, and real-world performance so you can pick the right model for your use case.

Head-to-head comparison

	M2.5	M2.7
Release	February 2026	March 2026
SWE-bench Verified	80.2%	~78%
SWE-Pro	—	56.22%
MMLU	88.1%	89.4%
HumanEval	91.5%	93.2%
Speed	~60 tok/s	100 tok/s
Input price	$0.15/1M	$0.30/1M
Output price	$0.55/1M	$1.10/1M
Self-evolving	❌	✅
Context window	200K	200K

M2.5 scores higher on SWE-bench Verified (80.2% vs ~78%) and costs half as much. M2.7 is faster, has self-evolving capability, and scores better on SWE-Pro, MMLU, and HumanEval.

Benchmark deep dive

The SWE-bench Verified gap is notable. M2.5’s 80.2% puts it ahead of most frontier models on pure code repair tasks. However, M2.7’s SWE-Pro score of 56.22% tells a different story — on harder, multi-file engineering problems, M2.7’s self-evolving architecture gives it an edge that M2.5 can’t match.

On general reasoning (MMLU 89.4%) and code generation (HumanEval 93.2%), M2.7 pulls ahead by small but consistent margins. The difference is most visible on tasks requiring iterative refinement, where M2.7’s self-evolving capability lets it correct its own mistakes mid-generation.

For a broader comparison across providers, see our AI model comparison page.

Pricing comparison

The cost difference is straightforward — M2.7 costs exactly 2x what M2.5 costs:

Metric	M2.5	M2.7
Input (per 1M tokens)	$0.15	$0.30
Output (per 1M tokens)	$0.55	$1.10
Typical coding session (50K in / 10K out)	$0.013	$0.026
1000 API calls (avg)	~$13	~$26

For high-volume batch processing — say, running code migrations across hundreds of files — M2.5 saves real money. For interactive coding sessions where you’re making a few dozen calls per day, the difference is negligible.

Both models are significantly cheaper than Claude 3.5 Sonnet or GPT-4o for equivalent coding tasks.

API examples

Both models are accessible through the OpenRouter API with identical interfaces. The only difference is the model identifier:

import openai

client = openai.OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="your-openrouter-key"
)

# Using M2.5 (cheaper, strong on SWE-bench)
response = client.chat.completions.create(
    model="minimax/minimax-m2.5",
    messages=[{"role": "user", "content": "Fix the null pointer exception in this code..."}],
    max_tokens=4096
)

# Using M2.7 (faster, self-evolving)
response = client.chat.completions.create(
    model="minimax/minimax-m2.7",
    messages=[{"role": "user", "content": "Refactor this module to use async/await..."}],
    max_tokens=4096
)

You can also use them directly with CLI tools:

# Aider with M2.7
aider --model openrouter/minimax/minimax-m2.7

# Aider with M2.5 for budget runs
aider --model openrouter/minimax/minimax-m2.5

When to use M2.5

• Pure coding tasks where SWE-bench score matters
• Budget is the primary concern ($0.15 vs $0.30 input)
• You don’t need the self-evolving feature
• High-volume batch processing (migrations, linting, bulk refactors)
• Latency isn’t critical (background jobs, CI pipelines)

When to use M2.7

• Agentic workflows that benefit from self-evolution
• Interactive coding where speed matters (100 tok/s vs 60 tok/s)
• Complex multi-step tasks requiring iterative reasoning
• You want the latest model with best general reasoning
• Real-time pair programming with tools like Aider

For a full breakdown of M2.7’s capabilities, see our MiniMax M2.7 complete guide.

The practical answer

For most developers, M2.7 is the better default — it’s faster and the self-evolving capability helps on complex tasks. Use M2.5 when you’re optimizing for cost on high-volume workloads.

Both are available on OpenRouter and work with Aider.

Real-world test

In a Kilo Code benchmark running both models on the same 10 coding tasks:

• M2.5 completed 8/10 tasks correctly
• M2.7 completed 8/10 tasks correctly (same success rate)
• M2.7 was 40% faster on average
• M2.5 cost 50% less total

The quality difference is minimal on straightforward tasks. The speed and self-evolving features of M2.7 matter more for interactive coding. M2.5 wins for batch processing where speed doesn’t matter.

Where M2.7 pulled ahead: on the 2 failed tasks, M2.7 got closer to a working solution. Its self-evolving capability meant it caught one of its own errors and partially corrected it, while M2.5 committed to the wrong approach without recovery.

Using both with model routing

The smartest approach: use M2.5 as your cheap model and M2.7 as your premium model. Both are from MiniMax so the coding style is consistent.

# Aider with model routing
aider --model openrouter/minimax/minimax-m2.7 --weak-model openrouter/minimax/minimax-m2.5

This gives you M2.7 for complex tasks and M2.5 for routine work — all under $0.30/1M average. See our model routing guide.

Use case recommendations

Use case	Recommended model	Why
Daily coding assistant	M2.7	Speed + self-evolving
Bulk code migration	M2.5	50% cheaper at scale
Code review automation	M2.5	Cost-effective, high accuracy
Agentic coding (multi-step)	M2.7	Self-evolving handles complexity
Quick prototyping	M2.7	100 tok/s feels instant
CI/CD integration	M2.5	Budget-friendly for automated runs

Want to see how MiniMax stacks up against other providers? Check our MiniMax M2.7 vs Claude vs DeepSeek comparison.

FAQ

Is MiniMax M2.7 better than M2.5?

It depends on the task. M2.7 is faster (100 tok/s vs 60 tok/s), has self-evolving capability, and scores higher on MMLU and HumanEval. However, M2.5 actually beats M2.7 on SWE-bench Verified (80.2% vs ~78%) and costs half as much. For most interactive coding, M2.7 is the better choice. For high-volume batch work, M2.5 offers better value.

Are MiniMax models free?

No, but they’re very affordable. M2.5 costs $0.15 per million input tokens and $0.55 per million output tokens. M2.7 costs $0.30/$1.10. A typical coding session costs around $0.01–$0.03. Both are available through OpenRouter with pay-as-you-go pricing — no subscription required.

Can I run MiniMax locally?

MiniMax has released some smaller models for local use, but the full M2.5 and M2.7 frontier models are too large to run on consumer hardware. You can run lighter MiniMax variants through Ollama for experimentation. See our guide on running MiniMax locally with Ollama for setup instructions.

How does MiniMax compare to DeepSeek?

MiniMax M2.7 and DeepSeek V3 are competitive on coding benchmarks, with MiniMax edging ahead on SWE-Pro (56.22%) and speed. DeepSeek tends to be cheaper and has stronger open-source community support. MiniMax’s self-evolving feature gives it an advantage on complex agentic tasks. For a detailed breakdown, see MiniMax M2.7 vs Claude vs DeepSeek.

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Related: MiniMax M2.7 Complete Guide · What is MiniMax? · MiniMax M2.7 vs Claude vs DeepSeek · AI Model Comparison